Chapter 1: General Introduction
Acknowledgment: Most of the knowledge comes from Yuan Yang's course "Machine Learning".
监督学习概况
Supervised learning is an important sub-area of machine learning.
Input: \(X = (x_1, x_2, \ldots, x_N)\)
Output: \(Y = (y_1, y_2, \ldots, y_N)\)
我们想要学习一个函数 f(x)，使得 \(f(x_i) \approx y_i\)。
要检测我们的模型学出来的函数好不好。
We define a loss function \(l\) to measure the distance of prediction \(f(X)\) to \(Y\):
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For categorical target (e.g., \(y\)=cat, dog, good, bad, classification):
- \(l(f,x_i, y_i) = 1\) if \(f(x_i) \ne y_i\)
- \(l(f,x_i, y_i) = 0\) if \(f(x_i) = y_i\)
- This is good but not differentiable. We will get back to it later.
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For real number target (e.g., \(y\)=0.1, 0.55, 1.5, regression):